The human brain can be modeled as a complex network or graph based on various connectivity metrics, such as functional connectivity (FC, denoting edge weights) that is interpreted as interactions or coordination among different brain regions (denoting nodes) ([1]}).
There have been various means to construct brain functional connectome, such as Pearson’s correlation of the resting-state fMRI (rs-fMRI) signals, and it has been recently extended to time-varying (non-stationary or dynamic) connectome, or chronnectome ([2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}). The FC or dynamic FC serve as sensitive non-invasive measurements to understand disease-related network alterations. Alzheimer’s Disease (AD) is generally regarded as a disconnection syndrome ([9]}) with gradual network topological changes in a prolonged period with a concealed onset ([10]}, [11]}, [12]}). Many recent efforts have been put forth to understand the neural underpinning of AD in its early, preclinical stage, also known as mild cognitive impairment (MCI) ([13]}, [14]}, [15]}, [16]}, [17]}), with brain network modeling using graph theory ([18]}).
